Nozzle performance analytics

ABSTRACT

A pick and place nozzle performance analytics system streams production data from pick and place machines used in electronic assembly to a cloud platform as torrential data streams, and performs analytics on the production data to track, visualize, and predict performance of individual nozzles in terms of rejects or miss-picks. The analytics system generates a performance vector for each nozzle based on the collected production data, the performance vector tracking both the accumulated rejects and the percentage of rejects as respective dimensions of an x-y plane. The system monitors and analyzes the trajectory of this vector in the x-y plane to predict when performance degradation of the nozzle will reach a critical threshold. In response to predicting that nozzle performance degradation will exceed a threshold at a future time, the system can generate and deliver notifications to appropriate client devices.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of, and claims priority to, U.S.patent application Ser. No. 16/111,712, filed on Aug. 24, 2018, andentitled “NOZZLE PERFORMANCE ANALYTICS,” the entirety of which isincorporated herein by reference.

BACKGROUND

The subject matter disclosed herein relates generally to industrialautomation, and, more particularly, performance analysis of pick andplace machines

BRIEF DESCRIPTION

The following presents a simplified summary in order to provide a basicunderstanding of some aspects described herein. This summary is not anextensive overview nor is intended to identify key/critical elements orto delineate the scope of the various aspects described herein. Its solepurpose is to present some concepts in a simplified form as a prelude tothe more detailed description that is presented later.

In one or more embodiments, a system for collecting and analyzing nozzleperformance data in terms of a component mis-pick or a componentrecognition failure is provided, comprising a data streaming componentconfigured to transfer production data collected from one or more pickand place machines to a cloud platform or an edge device as a datastream, wherein the production data comprises performance data forrespective nozzles of the one or more pick and place machines; a dataaggregation component configured to generate performance vector data fora nozzle, of the respective nozzles, based on the production data,wherein the performance vector data defines a location, over time, of avector point of the nozzle within an x-y plane, and the data aggregationcomponent determines the location of the vector point as a plot of atotal number of rejects for the nozzle on a y-axis of the x-y plane anda percentage of rejects for the nozzle on an x-axis of the x-y plane; avector analysis component configured to track a movement of the locationof the vector point based on an analysis of the performance vector data;and a notification component configured to generate notification datadirected to a specified client device in response to determining thatthe movement of the location of the vector point satisfies a definedcriterion.

Also, one or more embodiments provide a method for tracking nozzleperformance, comprising transferring, by a system comprising aprocessor, production data collected from one or more pick and placemachines to cloud-based storage as a data stream; generating, by thesystem, performance vector data for a nozzle, of the respective nozzles,based on the production data, the performance vector data defining alocation, as a function of time, of a vector point of the nozzle withinan x-y plane, wherein the generating comprises determining the locationof the vector point as a plot of a total number of rejects for thenozzle on a y-axis of the x-y plane and a percentage of rejects for thenozzle on an x-axis of the x-y plane; and in response to determiningthat a movement of the location of the vector point satisfies a definedcriterion, generating, by the system, notification data directed to aspecified client device.

Also, according to one or more embodiments, a non-transitorycomputer-readable medium is provided having stored thereon instructionsthat, in response to execution, cause a system to perform operations,the operations comprising transferring production data collected fromone or more pick and place machines to cloud-based storage as a datastream; generating performance vector data for a nozzle, of therespective nozzles, based on the production data, wherein theperformance vector data defines a location, as a function of time, of avector point of the nozzle within an x-y plane, and wherein thegenerating comprises determining, as the location of the vector point, aplot of a total number of rejects for the nozzle along a y-axis of thex-y plane and a percentage of rejects for the nozzle along an x-axis ofthe x-y plane; tracking the performance vector data to determine amovement of the location of the vector point over time; and in responseto determining that the movement of the location of the vector pointover time satisfies a defined criterion, generating, by the system,notification data directed to a specified client device.

To the accomplishment of the foregoing and related ends, certainillustrative aspects are described herein in connection with thefollowing description and the annexed drawings. These aspects areindicative of various ways which can be practiced, all of which areintended to be covered herein. Other advantages and novel features maybecome apparent from the following detailed description when consideredin conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of an example electronic assembly line.

FIG. 2 a high-level overview of an industrial enterprise that leveragescloud-based services.

FIG. 3 is a block diagram of an example nozzle performance analyticssystem.

FIG. 4 is a diagram illustrating an example architecture within whichembodiments of the nozzle performance analytics system can operate.

FIG. 5 is a high-level diagram illustrating data streaming and analysisof industrial data on a data lake.

FIG. 6 is a diagram illustrating application of analytics against a datastream by a data aggregation component and a vector analysis componentof a nozzle performance analytics system.

FIG. 7 is a diagram of an example edge device that can reside at theplant facility and provide data to the cloud-based analytics system.

FIG. 8 is a diagram illustrating an example data flow that can becarried out by a nozzle performance analytics system in connection withanalyzing nozzle performance.

FIG. 9 is an example graphical display that plots an accumulate numberof rejects and a percentage of rejects on an x-y graph for multiplenozzles.

FIG. 10 is an example graphical interface that can be generated by arendering component of a nozzle performance analytics system.

FIG. 11 is a flowchart of an example methodology for trackingperformance of pick and place nozzles and proactively notifyingpersonnel in response to predicting a nozzle performance degradation inexcess of an acceptable limit.

FIG. 12 is a flowchart of an example methodology for trackingperformance of pick and place nozzles and proactively altering controlof a pick and place machine to slow or mitigate nozzle performancedegradation.

FIG. 13 is an example computing environment.

FIG. 14 is an example networking environment.

DETAILED DESCRIPTION

The subject disclosure is now described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding thereof. It may be evident, however, that the subjectdisclosure can be practiced without these specific details. In otherinstances, well-known structures and devices are shown in block diagramform in order to facilitate a description thereof.

As used in this application, the terms “component,” “system,”“platform,” “layer,” “controller,” “terminal,” “station,” “node,”“interface” are intended to refer to a computer-related entity or anentity related to, or that is part of, an operational apparatus with oneor more specific functionalities, wherein such entities can be eitherhardware, a combination of hardware and software, software, or softwarein execution. For example, a component can be, but is not limited tobeing, a process running on a processor, a processor, a hard disk drive,multiple storage drives (of optical or magnetic storage medium)including affixed (e.g., screwed or bolted) or removable affixedsolid-state storage drives; an object; an executable; a thread ofexecution; a computer-executable program, and/or a computer. By way ofillustration, both an application running on a server and the server canbe a component. One or more components can reside within a processand/or thread of execution, and a component can be localized on onecomputer and/or distributed between two or more computers. Also,components as described herein can execute from various computerreadable storage media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry which is operated by asoftware or a firmware application executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can include a processor therein to executesoftware or firmware that provides at least in part the functionality ofthe electronic components. As further yet another example, interface(s)can include input/output (I/O) components as well as associatedprocessor, application, or Application Programming Interface (API)components. While the foregoing examples are directed to aspects of acomponent, the exemplified aspects or features also apply to a system,platform, interface, layer, controller, terminal, and the like.

As used herein, the terms “to infer” and “inference” refer generally tothe process of reasoning about or inferring states of the system,environment, and/or user from a set of observations as captured viaevents and/or data. Inference can be employed to identify a specificcontext or action, or can generate a probability distribution overstates, for example. The inference can be probabilistic—that is, thecomputation of a probability distribution over states of interest basedon a consideration of data and events. Inference can also refer totechniques employed for composing higher-level events from a set ofevents and/or data. Such inference results in the construction of newevents or actions from a set of observed events and/or stored eventdata, whether or not the events are correlated in close temporalproximity, and whether the events and data come from one or severalevent and data sources.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom the context, the phrase “X employs A or B” is intended to mean anyof the natural inclusive permutations. That is, the phrase “X employs Aor B” is satisfied by any of the following instances: X employs A; Xemploys B; or X employs both A and B. In addition, the articles “a” and“an” as used in this application and the appended claims shouldgenerally be construed to mean “one or more” unless specified otherwiseor clear from the context to be directed to a singular form.

Furthermore, the term “set” as employed herein excludes the empty set;e.g., the set with no elements therein. Thus, a “set” in the subjectdisclosure includes one or more elements or entities. As anillustration, a set of controllers includes one or more controllers; aset of data resources includes one or more data resources; etc.Likewise, the term “group” as utilized herein refers to a collection ofone or more entities; e.g., a group of nodes refers to one or morenodes.

Various aspects or features will be presented in terms of systems thatmay include a number of devices, components, modules, and the like. Itis to be understood and appreciated that the various systems may includeadditional devices, components, modules, etc. and/or may not include allof the devices, components, modules etc. discussed in connection withthe figures. A combination of these approaches also can be used.

Industrial controllers and their associated I/O devices are central tothe operation of modern automation systems. These controllers interactwith field devices on the plant floor to control automated processesrelating to such objectives as product manufacture, material handling,batch processing, supervisory control, and other such applications.Industrial controllers store and execute user-defined control programsto effect decision-making in connection with the controlled process.Such programs can include, but are not limited to, ladder logic,sequential function charts, function block diagrams, structured text, orother such programming structures.

Because of the large number of system variables that must be monitoredand controlled in near real-time, industrial automation systems oftengenerate vast amounts of near real-time data. In addition to productionstatistics, data relating to machine health, alarm statuses, operatorfeedback (e g , manually entered reason codes associated with a downtimecondition), electrical or mechanical load over time, and the like areoften monitored, and in some cases recorded, on a continuous basis. Thisdata is generated by the many industrial devices that make up a typicalautomation system, including the industrial controller and itsassociated I/O, telemetry devices for near real-time metering, motioncontrol devices (e.g., drives for controlling the motors that make up amotion system), visualization applications, lot traceability systems(e.g., barcode tracking), etc. Moreover, since many industrialfacilities operate on a 24-hour basis, their associated automationsystems can generate a vast amount of potentially useful data at highrates. The amount of generated automation data further increases asadditional plant facilities are added to an industrial enterprise.

Because this disparate automation data may reside on different types ofdevices (e.g., databases, industrial devices, etc.) and behindproprietary interfaces, integrating this data for the purposes of trendor root cause analysis can be challenging. Often, plant engineers mustbe physically present at a machine of interest in order to pull datadirectly from devices associated with the machine. Moreover, if the datais to be assimilated with production or supply chain data from othersources, plant engineers may have to run data queries on different datasources, and the results must be associated and merged before theassimilated data can be collectively analyzed. This process of manuallygathering and integrating data from different sources prevents effectivereal-time process monitoring and analysis of some types of industrialprocesses.

FIG. 1 is a diagram of an example electronic assembly line, a type ofindustrial system used to assembly electronic components such as printedcircuit boards (PCBs). This example assembly line includes a stencilprinter 102 that applies solder paste to incoming circuit boards, asolder paste inspection (SPI) station 104, a series of pick and placemachines 106 that mount surface-mounted components to the boards, aconnection reflow oven 108 that applies heat to the boards to meltapplied solder paste, and an automated optical inspection (AOI) station110 that uses a camera to verify quality of the final assembled PCB. Thepick and place machines 106 are sophisticated equipment that can beprogrammed to accommodate various sizes of circuit boards based onrequirements of a given PCB product. Each pick and place machine 106carries out a pick and place operation whereby the machine picks anelectronic component or part from a tape or tray and places thecomponent on the PCB at an appropriate location as directed by aplacement program executed by the machine. Pick and place machines 106can also capture images of the components and pass the resulting imagedata to a vision recognition system, which analyzes the image data toensure that the components are correctly located and oriented on eachnozzle.

To facilitate picking up and placing the electronic components, eachpick and place machine 106 includes multiple vacuum-based nozzles thatuse vacuum suction to pick up and hold the components until they areplaced on the boards. The nozzles mount to various spindles containedwithin a head that moves at a high speed. This equipment is roboticallycontrolled and can be programmed to optimally meet the board-layoutrequirements.

The pick and place operation is a data-rich procedure within the PCBmanufacturing process. Typically, data generated by the pick and placeoperation is stored in each pick and place machine's own database. Itwould be beneficial to leverage the knowledge conveyed in the datagenerated by the pick and place machinery to learn of potential defectsand other operational tendencies of interest that have yet to bediscovered. One such area of interest is the failure of components toproperly pick up and locate a component on the nozzle. For example,nozzle vacuum degradation (e.g., due to clogging of the nozzles,mechanical defects that worsen over time, etc.) increases the likelihoodof a failed or improper pick (referred to as a mis-pick or reject). Asnozzles degrade, more components are rejected and the opportunity tocreate a defect on the PCB increases. Depending on the nature of thedefect, a panel of multiple assembled electronic modules containingdefects may require up to 48 hours of delay in processing to rework insome cases. When evidence of nozzle mis-picks are discovered, aconsiderable amount of troubleshooting time is required to identifywhich nozzles should be cleaned or replaced. This can result in machinedowntime and thereby reduces the operating efficiency of the pick andplace machine 106.

If nozzle failures or performance degradations could be predicted,operators could be alerted to proactively repair or replace the nozzlesbefore mis-picks become a significant problem, thereby reducing thelikelihood of introducing defects into the finished products.Anticipating nozzle failure before the number of defects becomessignificant can also reduce downtime by mitigating the need to stopoperation in order to track down the source of defects after the nozzlehas failed.

To address these and other issues, one or more embodiments describedherein provide a nozzle performance analytics system capable ofperforming real-time and predictive performance assessments forindividual nozzles of pick and place machines. In one or moreembodiments, the analytics system can stream production data from pickand place machines used in electronic assembly to a cloud platform astorrential data streams, and perform analytics on the production data totrack, visualize, and predict performance of individual nozzles in termsof rejects or mis-picks. The analytics system generates a performancevector for each nozzle based on the collected production data. Theperformance vector tracks both the accumulated rejects and thepercentage of rejects as respective dimensions of an x-y plane. Thesystem monitors and analyzes the trajectory of this vector in the x-yplane for each nozzle to predict when performance degradation of thenozzle will reach a critical threshold. In response to predicting thatnozzle performance degradation will exceed a threshold at a future time,the system can generate and deliver notifications to selected clientdevices or operator interface terminals (e.g., human-machineinterfaces). In some embodiments, the system can also deliver controloutputs to a pick and place machine to alter operation of the machine ina manner that slows the rate of rejects as a result of the degradednozzle performance

In general, embodiments of the nozzle performance analytics describedherein can assess pick and place machine performance in two stages—adescriptive phase using a vector-based approach, and a predictive phaseusing machine learning that leverages the vector slope and rate ofchange to forecast future performance

Although the performance analytics system is described herein in thecontext of a pick and place machines used for electronic assembly (e.g.,assembly of printed circuit boards or other electronic components),embodiments of the performance analytics system described herein can beused to describe and predict performance of pick and place operationsused for other types of industrial applications. Moreover, although thepick and place machines described in the examples below use vacuum-basednozzles to pick and place components, the analytics system describedherein can also assess performance of other types of pick and placecomponents, including but not limited to robotic arms, actuators, orother components that implement pick and place functionality.

The nozzle analytics system described herein can execute as a service orset of services on a cloud platform in some embodiments. FIG. 2illustrates a high-level overview of an industrial enterprise thatleverages such cloud-based services. The enterprise comprises one ormore industrial facilities 204, each having a number of industrialdevices 208 and 210 in use. The industrial devices 208 and 210 can makeup one or more automation systems operating within the respectivefacilities 204. Exemplary automation systems can include, but are notlimited to, electronic assembly systems including pick and placemachines, batch control systems (e.g., mixing systems), continuouscontrol systems (e.g., PID control systems), or discrete controlsystems. Industrial devices 208 and 210 can include such devices asindustrial controllers (e.g., programmable logic controllers or othertypes of programmable automation controllers); field devices such assensors and meters; motor drives; operator interfaces (e.g.,human-machine interfaces, industrial monitors, graphic terminals,message displays, etc.); industrial robots, barcode markers and readers;vision system devices (e.g., vision cameras); smart welders; or othersuch industrial devices.

Exemplary automation systems can include one or more industrialcontrollers that facilitate monitoring and control of their respectiveprocesses. The controllers exchange data with the field devices usingnative hardwired I/O or via a plant network such as Ethernet/IP, DataHighway Plus, ControlNet, Devicenet, or the like. A given controllertypically receives any combination of digital or analog signals from thefield devices indicating a current state of the devices and theirassociated processes (e.g., temperature, position, part presence orabsence, fluid level, etc.), and executes a user-defined control programthat performs automated decision-making for the controlled processesbased on the received signals. The controller then outputs appropriatedigital and/or analog control signaling to the field devices inaccordance with the decisions made by the control program. These outputscan include device actuation signals, temperature or position controlsignals, operational commands to a machining or material handling robot,mixer control signals, motion control signals, and the like. The controlprogram can comprise any suitable type of code used to process inputsignals read into the controller and to control output signals generatedby the controller, including but not limited to ladder logic, sequentialfunction charts, function block diagrams, structured text, or other suchplatforms.

According to one or more embodiments, on-premise edge devices 206 cancollect data from industrial devices 208 and 210—or from other datasources, including but not limited to data historians, business-levelsystems, etc.—and send this data to cloud platform 202 for processingand storage. Cloud platform 202 can be any infrastructure that allowscloud services 212 to be accessed and utilized by cloud-capable devices.Cloud platform 202 can be a public cloud accessible via the Internet bydevices having Internet connectivity and appropriate authorizations toutilize the services 212. In some scenarios, cloud platform 202 can beprovided by a cloud provider as a platform-as-a-service (PaaS), and theservices 212 (such as the alarm annunciation brokering system describedherein) can reside and execute on the cloud platform 202 as acloud-based service. In some such configurations, access to the cloudplatform 202 and the services 212 can be provided to customers as asubscription service by an owner of the services 212. Alternatively,cloud platform 202 can be a private or semi-private cloud operatedinternally by the enterprise, or a shared or corporate cloudenvironment. An exemplary private cloud can comprise a set of servershosting the cloud services 212 and residing on a corporate networkprotected by a firewall.

Cloud services 212 can include, but are not limited to, data storage,data analysis (including pick and place nozzle failure analysis),control applications (e.g., applications that can generate and delivercontrol instructions to industrial devices 208 and 210 based on analysisof real-time system data or other factors), performance monitoring(including pick and place machine performance monitoring), expertisebrokering services, visualization applications such as the cloud-basedoperator interface system described herein, reporting applications,Enterprise Resource Planning (ERP) applications, notification services,or other such applications. Cloud platform 202 may also include one ormore object models to facilitate data ingestion and processing in thecloud. If cloud platform 202 is a web-based cloud, edge devices 206 atthe respective industrial facilities 204 may interact with cloudservices 212 directly or via the Internet. In an exemplaryconfiguration, the industrial devices 208 and 210 connect to theon-premise edge devices 206 through a physical or wireless local areanetwork or radio link. In another exemplary configuration, theindustrial devices 208 and 210 may access the cloud platform 202directly using integrated cloud agents.

Ingestion of industrial device data in the cloud platform 202 can offera number of advantages particular to industrial automation. For one,cloud-based storage offered by the cloud platform 202 can be easilyscaled to accommodate the large quantities of data generated daily by anindustrial enterprise. Moreover, multiple industrial facilities atdifferent geographical locations can migrate their respective automationdata to the cloud for aggregation, collation, collective analysis,visualization, and enterprise-level reporting without the need toestablish a private network between the facilities. Edge devices 206 canbe configured to automatically detect and communicate with the cloudplatform 202 upon installation at any facility, simplifying integrationwith existing cloud-based data storage, analysis, or reportingapplications used by the enterprise. In another example application,cloud-based diagnostic applications can monitor the health of respectiveautomation systems or their associated industrial devices across anentire plant, or across multiple industrial facilities that make up anenterprise. Cloud-based lot control applications can be used to track aunit of product through its stages of production and collect productiondata for each unit as it passes through each stage (e.g., barcodeidentifiers, production statistics for each stage of production, qualitytest data, abnormal flags, etc.). Moreover, cloud-based controlapplications can perform remote decision-making for a controlledindustrial system based on data collected in the cloud from theindustrial system, and issue control commands to the system via thecloud agent. These industrial cloud-computing applications are onlyintended to be exemplary, and the systems and methods described hereinare not limited to these particular applications. The cloud platform 202can allow software vendors to provide software as a service, removingthe burden of software maintenance, upgrading, and backup from theircustomers.

FIG. 3 is a block diagram of an example nozzle performance analyticssystem 302 according to one or more embodiments of this disclosure.Aspects of the systems, apparatuses, or processes explained in thisdisclosure can constitute machine-executable components embodied withinmachine(s), e.g., embodied in one or more computer-readable mediums (ormedia) associated with one or more machines. Such components, whenexecuted by one or more machines, e.g., computer(s), computingdevice(s), automation device(s), virtual machine(s), etc., can cause themachine(s) to perform the operations described.

Nozzle performance analytics system 302 can include one or more datastreaming components 304, a data aggregation component 306, a vectoranalysis component 308, a notification component 310, a renderingcomponent 312, a user interface component 314, one or more processors318, and memory 320. In various embodiments, one or more of the datastreaming component(s) 304, data aggregation component 306, vectoranalysis component 308, notification component 310, rendering component312, user interface component 314, the one or more processors 318, andmemory 320 can be electrically and/or communicatively coupled to oneanother to perform one or more of the functions of the nozzleperformance analytics system 302. In some embodiments, components 304,306, 308, 310, 312, and 314 can comprise software instructions stored onmemory 320 and executed by processor(s) 318. Nozzle performanceanalytics system 302 may also interact with other hardware and/orsoftware components not depicted in FIG. 3. For example, processor(s)318 may interact with one or more external user interface devices, suchas a keyboard, a mouse, a display monitor, a touchscreen, or other suchinterface devices.

The one or more data streaming components 304 can be configured tomanage migration of data from pick and place equipment at one or moreindustrial facilities to the cloud platform. In one or more embodiments,the system can facilitate migration of data from industrial data sourceson the plant floor using produce clients and consume clients, whichyield torrential data streams from the plant floor to a data lake on thecloud platform. The data aggregation component 306 can be configured toharmonize and normalize the collected data for collective analysis. Thiscan include generating real-time and time-series performance vector datafor respective pick and place machines—or for respective nozzles of eachmachine—based on both the total accumulated number of part rejects(e.g., mis-picks) and the percentage of part rejects calculated for themachine. Vector analysis component 308 can be configured to analyze themachine-specific performance vectors to predict future operationaltrends or future times of failure for pick and place nozzles or heads.

Notification component 310 can be configured to generate a notificationdirected to one or more recipient client devices in response todetecting a present or predicted issue with a nozzle or its associatedpick and place machine. Rendering component 312 can be configured togenerate dashboards or other types of graphical interfaces that renderitems of collected data as well as analysis results.

User interface component 314 can be configured to exchange informationbetween the system 302 and a client device associated with an authorizeduser of the system 302. To this end, user interface component 314 can beconfigured to serve user interface screens to the client device thatallow the user to view information stored or generated by the system 302(e.g., dashboards rendering nozzle statistics in both textual andgraphical formats to be discussed herein, alarm status information,etc.) and to send information to the system 302 (e.g., plant, machine,and nozzle selection input, etc.).

The one or more processors 318 can perform one or more of the functionsdescribed herein with reference to the systems and/or methods disclosed.Memory 320 can be a computer-readable storage medium storingcomputer-executable instructions and/or information for performing thefunctions described herein with reference to the systems and/or methodsdisclosed.

FIG. 4 is a diagram illustrating an example architecture within whichembodiments of the nozzle performance analytics system 302 can operate.In this example architecture, nozzle performance analytics system 302executes as a service on a cloud platform, and exchanges data withindustrial devices at a plant facility—including devices associated withpick and place machines 106—via a plant network 416 accessible to thecloud platform (e.g., a wired or wireless network). However, in otherimplementations the components of nozzle performance analytics system302 can execute on one or more servers that reside within the plantfacility itself and exchange data with industrial devices via plantnetwork 416.

The one or more data streaming components 304 of analytics system 302gather machine data 404 from data sources associated with the pick andplace machines 106. This can include, for example, real-time andhistorical statistical and operational data stored in local databasesassociated with the machines 106. Example data values that can becollected and analyzed from the pick and place machines 106 can include,but are not limited to, a total number of parts picked by each nozzle ofeach pick and place machine, a total number of rejected parts (ormis-picks) for each nozzle, an estimated number of missing parts foreach nozzle, cycle times for each pick and place machine, alarm data foreach machine, and other such information. Although only a single set ofpick and place machines 106 is depicted in FIG. 4, analytics system 302can be configured to collect data from multiple pick and place machines106 distributed throughout a plant facility, or across multiplegeographically diverse facilities.

The collected data is harmonized, normalized, and collectively analyzedby the analytic system's data aggregation component 306 and vectoranalysis component 308, as will be described below. As part of thisprocessing, analytic system 302 can create a performance vector for eachnozzle can detect and/or perform predictive analysis on these vectors todetermine whether any of the nozzles are at risk of failure or ofdegraded performance Rendering component 312 can generate performancedisplay interfaces 408 that render historical and real-time statisticsfor the monitored pick and place machines and deliver these interfacesto authorized client devices 410 (e.g., HMI terminals; personal mobiledevices; laptop, desktop, or tablet computers, etc.) Also, in responseto determining, based on the predictive analysis, that a nozzle is atrisk of failure or degraded performance, notification component 310 caninstruct the rendering component 312 to deliver one or morenotifications 406 to selected client devices of personnel associatedwith the relevant pick and place machines 106, thereby notifying therecipient of the risk and recommending one or more countermeasures.

In some embodiments, analytics system 302 can also be configured todeliver control outputs 402 directed to selected control devices inresponse to detecting or predicting a nozzle failure or performancedegradation. The control output 402 can be configured to alter operationof a selected pick and place machine 106 to either mitigate theidentified performance issue or to slow the performance degradationuntil the defective nozzle can be repaired or replaced. For example, thecontrol output 402 can be configured to alter an operation of the pickand place machine in a manner that slows the nozzle's current rate ofrejected parts.

In some embodiments, the nozzle analytics architecture can manage one ormore data pipelines that migrate data from plant facilities to a datalake residing on the cloud platform. For systems in which data frommultiple different industrial enterprises is monitored, the data streamscan be segregated by industrial enterprise (customer) and can further besegregated according to any other suitable criterion (e.g., plantfacility, production area, etc.). In an example implementation, thenozzle performance analytics system 302 described herein can implement asubscription layer that harmonizes and pushes data from pick and placemachines 106 at one or more industrial sites to a data lake leveraged byanalytics system 302. The analytics system 302 can migrate data fromvarious industrial sites as distinct data and apply customer-specificrules to each data stream for various purposes prior to storage andanalysis on the cloud platform. Performing data streaming and analyticson a data lake can facilitate system flexibility and scalability, sinceheterogeneous data can be stored in a raw format without extensivepreprocessing (e.g., extract transform load, or ETL, processing). FIG. 5is a high-level diagram illustrating data streaming and analysis ofindustrial data—including pick and place machine data—on a data lake 502according to one or more embodiments. In this example, data lake 502resides on a cloud infrastructure (e.g., cloud platform 202, a privatecloud, or a public cloud that offers infrastructure-as-a-service).

Nozzle analytics system 302 works in connection with the data lake 502to stream data from a variety of industrial data sources 504, includingbut not limited to pick and place machines and associated control andstorage devices, industrial robots, motor drives (e.g., variablefrequency drives or other types of drives), industrial controllers, orindustrial machines or their associated control devices. The datastreaming services implemented by system 302 can perform nozzleanalytics on individual data streams in some embodiments. Nozzleperformance analysis can be carried out on the data lake usingdistributed processing techniques made possible by the scalablecomputing resources of the data lake 502. In some embodiments, afterstream analysis has been performed on the data streams, the data and anyanalytical results can be either stored on cloud-based storage incustomer-specific data storage, or can be placed in defined data queuesfor queue analysis. A number of different types of applications 506 canleverage the data and analysis results generated and stored on the cloudplatform, including but not limited to reporting applications,interactive web and mobile applications, enterprise applications, etc.

As noted above, some embodiments of nozzle performance analytics system302 can perform stream-level analysis on the industrial data streamsthat migrate data from the industrial sites to the cloud system. FIG. 6is a diagram illustrating application of analytics against a data streamby data aggregation component 306 and vector analysis component 308. Inthis example, the data streaming components 304 of nozzle performanceanalytics system 302 (e.g., one or more produce clients and consumeclients) migrate data generated by one or more data sources at anindustrial facility (i.e., a customer site) to cloud-based big datastorage 606 by streaming the data from the data sources to the cloud astorrential data, yielding a data stream 604. The streamed data caninclude, for example, time-series metric data generated by sensors orother industrial devices on the plant floor (e.g., pick and placemachine components, vacuum or pressure sensors, temperature sensors,flow meters, level sensors, proximity switches, etc.); inspection resultdata generated by automatic optical inspection station 110; alarm datagenerated by an industrial controller, a motor drive, a safetycontroller, a quality check system, etc.; or other types of data.

In some embodiments, the data stream 604 can comprise data collectedfrom the respective industrial devices at the plant facility by adedicated on-premise edge device (e.g., edge devices 206 illustrated inFIG. 2), which interfaces with the nozzle performance analytics system302 to facilitate streaming of torrential data to the cloud-base system.Turning briefly to FIG. 7, an example edge device 206 that can reside atthe plant facility and provide data to the cloud-based analytics system302 is illustrated. In this example technique, on-premise datacollection is enabled by a collection of services that function toprocess and send collected industrial data to the cloud-based datapipeline. Data concentrator 728 and edge device 206 respectivelyimplement two main functions associated with data collection—dataconcentration using a historian 738 and associated data storage 736(e.g., an SQL server or other type of storage), and cloud dataenablement using cloud agent services executed by edge device 206. Plantdata 710 from one or more industrial devices (e.g., controllers anddatabases associated with pick and place machines) is collected by dataconcentrator 728 at the plant facility. In an example scenario, plantdata 710 may comprise stamping press time-series sensor data, made up ofthousands of data points updated at a rate of less than a second. Plantdata 710 can also comprise alarm data generated by one or moreindustrial devices in response to detected alarm events.

Collection services component 702 of edge device 206 implementscollection services that collect device data, either from dataconcentrator's associated data storage (e.g., via an SQL query) ordirectly from the devices themselves via a common industrial protocol(CIP) link or other suitable communication protocol. For example, toobtain data from data concentrator 728, collection services component702 may periodically run a data extraction query (e.g., an SQL query) toextract data from data storage 736 associated with data concentrator728. Collection services component 702 can then compress the data andstore the data in a compressed data file 712. Queue processing servicesexecuted by queue processing component 704 can then read the compresseddata file 712 and reference a message queuing database 714, whichmaintains and manages customer-specific data collection configurationinformation, as well as information relating to the customer'ssubscription to the cloud platform and associated cloud services. Basedon configuration information in the message queuing database 714, queueprocessing component 704 packages the compressed data file 712 into adata packet and pushes the data packet to the nozzle performanceanalytics system 302 on the cloud platform. In conjunction with the datastreaming components 304 of analytics system 302, the edge device 206can inject the data as torrential data 716.

Message queuing database 714 can include site-specific informationidentifying the data items to be collected (e.g., data tag identifiers),user-defined processing priorities for the data tags, firewall settingsthat allow edge device 206 to communicate with the cloud platformthrough a plant firewall, and other such configuration information.Configuration information in message queuing database 714 can instructedge device 206 how to communicate with the identified data tags andwith the remote data collection services on the cloud platform.

In addition to collection and migration of data, one or more embodimentsof edge device 206 can also perform local analytics on the data prior tomoving the data to the cloud platform. This can comprise substantiallyany type of pre-processing or data refinement that may facilitateefficient transfer of the data to the cloud platform, prepare the datafor enhanced analysis in the cloud platform, reduce the amount of cloudstorage required to store the data, or other such benefits. For example,edge device 206 may be configured to compress the collected data usingany suitable data compression algorithm prior to migrating the data tothe cloud platform. This can include detection and deletion of redundantdata bits, truncation of precision bits, or other suitable compressionoperations. In another example, edge device 206 may be configured toaggregate data by combining related data from multiple sources. Forexample, data from multiple sensors measuring related aspects of anautomation system can be identified and aggregated into a single cloudupload packet by edge device 206. Edge device 206 may also encryptsensitive data prior to upload to the cloud. In yet another example,edge device 206 may filter the data according to any specified filteringcriterion (e.g., filtering criteria defined in a filtering profilestored on the edge device). For example, defined filtering criteria mayspecify that measured pressure values exceeding a defined setpoint areto be filtered out as outliers prior to uploading the pressure values tothe analytics system 302.

Edge device 206 may also associate metadata with selected subsets of thedata prior to migration to the cloud platform, thereby contextualizingthe data within the industrial environment. For example, edge device 206can tag selected subsets of the data with a time indicator specifying atime at which the data was generated, a quality indicator, a productionarea indicator specifying a production area within the industrialenterprise from which the data was collected, a machine or process stateindicator specifying a state of a machine or process at the time thedata was generated, a personnel identifier specifying an employee onduty at the time the data was generated, or other such contextualmetadata. In some embodiments, the edge device 206 can also aggregatethe data with external data retrieved from external sources (e.g.,weather data, stock market price data, etc.) In this way, edge device206 can perform layered processing of the collected data to generatemeta-level knowledge that can subsequently be leveraged by cloud-basedanalysis tools to facilitate enhanced analysis of the data in view of alarger plant context.

To ensure secure outbound traffic to the cloud, one or more embodimentsof edge device 206 can support HTTPS/SSL, certificate authority enabledtransmission, and/or unique identity using MAC addresses. Edge device206 can also support store-and-forward capability to ensure data is notlost if the edge device 206 becomes disconnected from the cloudplatform.

Returning now to FIG. 6, the data stream 604 can be customer-specific,and may include data from multiple different devices, machines, and/orfacilities. As the data is being streamed from the plant facility tocloud-based storage 606, data aggregation component 306 and vectoranalysis component 308 of nozzle performance analytics system 202 canprocess selected subsets of the data in connection with assessingperformance of respective pick and place machines, including nozzleperformance statistics, as will be described in more detail below. Basedon results of this analysis, rendering component 312 and notificationcomponent 310 can generate and deliver performance display interfaces408 and notifications 406 to selected client devices, as well as controloutputs 402 directed to selected control devices on the plant floor.

FIG. 8 is a diagram illustrating an example data flow that can becarried out by the nozzle performance analytics system 302 in connectionwith analyzing nozzle performance As described above, production data812 generated by control devices associated with pick and place machines106 is collected and streamed to the cloud platform on which analyticssystem 302 operates (e.g., to a data lake on the cloud platformaccessible to the analytics system 302). Data collected from the pickand place machinery can include, but is not limited to, a number ofparts or components picked up by each nozzle of each pick and placemachine, the number of rejected parts due to missed picks by eachnozzle, a number of possible missing parts for each nozzle, and othersuch information. This data can comprise cumulative values for eachnozzle that are accumulated across batches for the duration of aproduction run (in an example production scenario, each production runmay comprise a cycle of approximately 10 minutes). In some embodiments,production data 812 may be migrated to the analytics system 302substantially in real time as each monitored value is updated by thepick and place devices. Alternatively, updated values of production data812 may be migrated to the analytics system 302 only at the conclusionof each batch or production run.

Data aggregation component 306 uses production data 812 to calculate anumber of production statistics for each nozzle of each pick and placemachine 106. This can include tracking the total accumulated number ofrejects and successful picks, as well as calculating performance metricsbased on this information, including a reject percentage for eachnozzle. The reject percentage can be computed in various ways dependingon user preference or depending on the type of statistical dataavailable from each pick and place machine 106. For example, the rejectpercentage may be calculated as a percentage of the total attemptedpics, as given by:

% Reject=[# rejects]/([# rejects]+[# successful picks])   (1)

If production data 812 includes a cumulative value of an estimatednumber of missing parts, in addition to successful picks and rejectedpicks, the rejected percentage can be calculated according to:

% Reject=[# rejects]/([# rejects]+[# successful picks]+[# missingpicks])   (2)

In yet another example, the reject metric may be calculated as a ratioof rejected parts to successful picks, as given by:

% Reject=[# rejects]/([# successful picks])   (3)

In some embodiments, data aggregation component 306 can be configured topre-process the raw production data 812 prior to analysis. This caninclude, for example, identifying and removing outlier values that mayrepresent invalid measurements, normalizing the production data 812 to acommon format that can be collectively analyzed by the data aggregationcomponent 306, or other such pre-processing.

To facilitate analysis of the behavior of each nozzle over time, as wellas overall performance of each pick and place machine over time, dataaggregation component 306 can generate performance vector data 802 foreach monitored nozzle. In the example depicted in FIG. 8, performancevector data 802 is obtained by plotting, for each nozzle, the totalnumber of rejects against the percentage of rejects, and monitoring thetrajectory of this plot over time. The total accumulated number ofrejects is plotted on the y-axis, and the percentage of rejects(represented by any of equations (1)-(3) above, or reasonable variationsthereof) is plotted on the x-axis. In various embodiments, one or bothof the total rejects (the y-axis position) or the percentage of rejects(the x-axis position) may be calculated based on all accumulatedproduction data received since a most recent maintenance operation orreplacement of the nozzle, or alternatively may be calculated based on afixed window of most recent production data (e.g., the most recent Nproduction cycles, where N is an integer). Moreover, performancestatistics can be tracked as a function of the specific pick and placeprogram executed by the pick and place machines 106, and the totalreject and percentage reject values may represent the reject statisticsfor the nozzle when operated in accordance with the specific program, aswill be discussed below.

Vector analysis component 308 can analyze the performance vector data802 for each nozzle over time in order to track the performance behaviorof the nozzle as a function of time, and in some embodiments to learn apattern of behavior for the nozzle. FIG. 9 is an example graphicaldisplay 902 that plots the accumulate number of rejects and thepercentage of rejects on an x-y graph for multiple nozzles. Renderingcomponent 312 can generate graphical display 902 based on statisticaldata 806 generated by vector analysis component 308 based on performancevector data 802. Graphical display 902 renders the nozzle information inthe form of a bubble chart on the x-y graph in this exampleimplementation, where each bubble 904 represents a single nozzle. Thelocation of each nozzle-specific bubble 904 in the x-y plane is alsoreferred to as a vector point for the nozzle. The nozzle bubbles 904 canbe color coded according to any suitable distinguishing characteristic.For example, bubbles 904 may be color coded according to the particularpick and place machine the nozzles are associated with, according to adegree of performance deterioration calculated for the nozzle (e.g., atotal accumulated number of rejects or mis-picks), or other suchdistinguishing criteria. Performance analytics system 302 is capable ofperforming performance analysis on multiple nozzles concurrently andupdating the graphical display 902 accordingly for the multiple nozzlescurrently being displayed (e.g., the N nozzles that make up a selectedpick and place machine, where N is an integer).

By definition of the x-y graph depicted in FIG. 9, placement of a nozzlebubble 904 at the origin of the graph represents flawless nozzleperformance whereby no rejects have been accumulated for thecorresponding nozzle. As the nozzle begins rejecting parts (e.g.,failing to correctly pick up a part or component due to such factors asimproper vacuum levels, wear and tear on the mechanical nozzlecomponents, environmental conditions such as humidity or pollutants thatnegatively impact the nozzle's ability to correctly pick up a part orcomponent, etc.), the bubble 904 for that nozzle will begin to move awayfrom the origin over time. Specifically, the bubble 904 for each nozzlewill move upward in the y-axis directs as a function of the accumulatednumber of rejects for the nozzle, and will move left and right along thex-axis direction as a function of the percentage of rejects for thatnozzle (e.g., as determined based on any of equations (1)-(3)).Typically, until the total number of rejects for a given nozzle arereset, the bubble 904 for that nozzle will move along the x-axis only inthe upward direction, since the total number of rejects is a cumulativevalue that only increases (and does not decrease) over time. Incontrast, movement of the bubble 904 along the x-axis may oscillate,since the percentage of rejects may vary in both directions over time asmore pick operations—both successful and failed—are accumulated.However, if the nozzle is experiencing an issue that degradesperformance over time, movement of the bubble 904 along the x-axis islikely to trend rightward. In general, as the bubble 904 (or vectorpoint) for a nozzle moves away from the origin, increasing the risk ofmore defects to be introduced into the assembled PCB.

In addition to rendering these nozzle-specific reject statisticsgraphically, vector analysis component 308 can track the movement ofthese statistics over time for each nozzle in order to learn near-termand long-term behaviors for each nozzle. By analyzing nozzle rejecttrends in this manner, nozzle performance analytics system 302 candetect and/or predict performance trends and expected times of failurefor each nozzle, as well as identify correlations between specificnozzle configurations and part mis-picks that would otherwise beinvisible if only instantaneous measurements of nozzle performance wereexamined.

In an example scenario, during a production run, data aggregationcomponent 306 updates the performance vector data 802 for each nozzleinvolved in the production run based on any additional accumulatedrejects for the nozzle (the y-axis position) as well as updatedcalculated values of the reject percentage (the x-axis position). Theperformance vector data 802 can be updated either continuously or on aperiodic basis depending on how frequently new production data 812 ismigrated to the analytics system 302. As the performance vector data 802is updated in real time, vector analysis component 308 re-positions thenozzle's current vector point (represented by bubble 904 on graphicaldisplay 902) on the x-y plane to reflect the current vector data values.Vector analysis component 308 also maintains a record of past positionsof the nozzle's vector point within the x-y plane so that the nozzle'sperformance as a function of time can be assessed.

Based on the time-series movement of the vector point for a givennozzle, vector analysis component 308 can identify when a characteristicof the movement of the nozzle's vector point is indicative of aperformance degradation requiring a maintenance action. Detection of apotential performance issue can be based on one or more characteristicsof the vector point's movement, including but not limited to thedirection of movement of the nozzle's vector point within the x-y plane,the rate of change of the vector point's movement in the x-y plane, orother vector characteristics. Because of the structure of the x-y plane,whereby cumulated rejects are plotted on the y-axis and percentagerejects are plotted on the x-axis, the direction and/or rate of changeof each nozzle's vector point in this x-y plane can be monitored toidentify possible performance degradations. For example, movement of thevector point right-ward (the positive x direction) is indicative of anincrease in the reject rate, and the rate at which the vector moves inthis direction also conveys a severity of this performance issue.Movement of the vector point upward (the positive y direction) with nomovement rightward may pose no concern, since the rate of rejection isremaining consistent (or may be reducing if the vector point issimultaneously moving to the left). The level of concern when the vectorpoint is moving in upward and leftward simultaneously may depend on theparticular direction of movement as well as the rate of change of thevector point in this direction.

Given these considerations, as well as the specifics of the particularpick and place applications, a range of directions of movementindicative of a performance concern can be defined in the system 302.The range of directions can be defined in terms of angles of movementrelative to the origin of the x-y plane, or using another directionalnomenclature. In addition or alternatively, a rate of change or anincremental step change indicative of a performance concern can bedefined in the system 302. For embodiments in which both directionalranges and rates of changes are defined, the system 302 can allowcombinations of both direction and rate of change to be defined that, ifdetected, are indicative of a performance concern requiring attention.These vector characteristic definitions serve as trigger criteria forvector analysis component 308.

Once these critical vector characteristics are defined, vector analysiscomponent 308 can monitor the directions of movement of the vectorpoints (represented by bubbles 904) of multiple nozzles to determinewhether movement of any of the vectors correspond to the critical vectorcharacteristics—in terms of one or both of direction or rate ofchange—that were previously defined. In some embodiments, vectoranalysis component 308 can track the movement of each nozzle's vectorpoint over a fixed duration up to the present time (e.g., the movementof the vector point over the last ten minutes, or another duration), sothat the nozzle's current direction and/or rate of change can becalculated based on a most recent set of performance data. In responseto determining that one or both of the direction or the rate of changeof the vector point corresponds to a defined critical direction or rateof change (or combination thereof), notification component 310 cangenerate a notification directed to one or more selected client devices410 associated with authorized plant personnel associated with theparticular assembly line in which the nozzle operates, as will bediscussed in more detail below. System 302 may also be configured tosend a control output 402 in response to detection of a performanceissue intended to alter performance of the corresponding pick and placemachine to either slow the performance degradation of the identifiednozzle or to remove the nozzle from the present pick and placeoperation.

In addition to tracking the present direction and/or rate of change ofthe vector point, some embodiments of vector analysis component 308 canalso track the movement of the nozzle's vector point within the x-yplane over time as a vector, and predict a future trend of the vectorpoint based on analysis of the direction, magnitude, and speed of thisvector. In FIG. 9, the predicted future trend for an example nozzlerepresented by bubble 904 is represented by arrow 910. In someembodiments, rendering component 312 can be configured to plot arrow 910representing the predicted future trajectory for a selected nozzle(e.g., in response to selection of the nozzle by the user viainteraction with the graphical display).

Based on analysis of the predicted future reject trend represented byarrow 910—and in particular the direction, magnitude, and speed or rateof change of the future trend—vector analysis component 308 can predictwhen the performance of the nozzle, in terms of rejected parts ormis-picks, will deteriorate to a point at which a maintenance actionwill be required to correct the issue. To this end, analytics system 302can allow end users to define (via user interface component 314)performance thresholds representing upper limits on acceptable number ofaccumulated rejects for a nozzle and acceptable reject percentages for anozzle. Rendering component 312 can render the user-defined total rejectupper limit on graphical display 902 as horizontal reject quantityboundary line 906. Similarly, rendering component 312 can render theuser-defined reject percentage upper limit on graphical display 902 asvertical reject percentage boundary line 908. By visualizing thenozzle-specific bubbles 904 on the x-y plane relative to boundary lines906 and 908, graphical display 902 provides a visual indication of howclose each nozzle's performance is the maximum allowable number ofaccumulated rejects and the maximum allowable reject percentage.

In addition to visualizing these nozzle performance statistics, nozzleperformance analytics system 302 can also be configured to generate anddeliver reactive or proactive notifications 406 to selected clientdevices in response to a determination by vector analysis component 308,based on analysis on the predicted future trend of a nozzle (representedby arrow 910), that the nozzle's performance is at risk of reaching theuser-defined reject boundaries represented by lines 906 and 908, oranother preconfigured threshold. For example, based on predictiveanalysis performed on the expected future trend for a given nozzle,vector analysis component 308 can estimate future x-y positions of thevector point (graphically represented by bubble 904), and therebyestimate a future time at which the nozzle will exceed either themaximum number of acceptable accumulated rejection (boundary line 906)or the maximum acceptable reject percentage (boundary line 908). Thefuture x-y positions can be predicted based on analysis of past x-ypositions up to the present moment, as well as past rates of change ofthe x-y positions and the directions of the position changes, which canbe combined to compute the estimated future trend vector of the nozzle'sperformance (arrow 910) from the present moment through a future momentrepresenting the limit of accurate prediction. Any suitable techniquefor performing predictive analysis on the performance vector data 802for a nozzle is within the scope of one or more embodiments of thisdisclosure (e.g., analysis of the slope of the past and predicted futurevector represented by performance vector data, as well as analysis ofthe rate of change of the vector). In some embodiments, analytics system302 can be configured to regulate the formation of false positivedetections, since a given nozzle may change its performance vector in abenign direction, inhibiting its previous state.

In response to determining that the duration between the present timeand the expected future time at which the nozzle's performance isexpected to reach a reject boundary is less than a notificationthreshold (that is, the duration until the nozzle's performance isexpected to degrade to an unacceptable level is sufficiently small towarrant a maintenance action), notification component 310 can generate anotification directed to one or more selected client devices 410associated with authorized plant personnel associated with theparticular assembly line in which the nozzle operates.

Notifications generated by notification component 310 in response toeither a real-time or predicted performance issue can identify suchinformation as an identity of the pick and place machine in which thenozzle operates, an identity of the faulty nozzle, an expected timeuntil the nozzle's performance is expected to reach one of the rejectboundaries, a current number of accumulated rejects for the nozzle, acurrent reject percentage for the nozzle, a recommended countermeasurefor correcting the nozzle's performance (e.g., replacement of thenozzle, correction of vacuum levels, etc.).

In addition to generating proactive notifications based on thepredictive analysis described above, performance analytics system 302can also be configured to generate reactive notifications in response todetermining that a vector's performance vector has crossed one of thethresholds. Such reactive notifications can identify the pick and placemachine containing the degraded nozzle, as well as the particular nozzlewhose performance has deteriorated beyond the defined threshold.

In some embodiments, as analytics system 302 tracks cumulative behaviorand performance of each nozzle over time, vector analysis component 308can generate performance model data 814 for each nozzle. Thisperformance model data 814 represents expected behavior of the modelover time, and can be generated based on monitored historicalperformance of each nozzle. Performance model data 814 can model suchbehavior characteristics as expected rates of performance degradationover time (where the rates of degradation may themselves change over thenozzle's lifecycle), expected vector curves in the x-y plane (that is,the plane of accumulated rejects plotted against the percentagerejects), expected reject rates at given times within the nozzle's lifecycle, or other such behavior characteristics. Vector analysis component308 can update performance model data 814 for each nozzle as newproduction data 812 is received and analyzed. This accumulated modeldata 814 can be referenced by vector analysis component 308 inconnection with predicting future performance trends for the nozzlegiven current behaviors of the nozzle.

Some pick and place machines 106 are capable of executing different pickand place programs, each of which is designed to assemble a specifictype of product (e.g., a type of printed circuit board or other type ofelectronic assembly). In some cases, different pick and place programsmay employ different subsets of available nozzles within the pick andplace machine, and some nozzles' roles in the assembly process may varybetween the programs. For example, a first pick and place program thatexecutes on a machine may comprise a set of 20 nozzle, while a secondprogram may employ a different set of 20 individual nozzles orcombination of some original nozzles and other nozzles that are trackedby a unique identifier. Moreover, a nozzle that participates in multipledifferent assembly programs may be programmed to behave differently ineach program (e.g., the path of movement of the nozzle may varydepending on the program). As such, the expected performance of a givennozzle may depend in part on the particular pick and place program thatis currently controlling the nozzle.

Accordingly, in some embodiments, in addition to tracking and predictingcumulative nozzle performance across all programs (that is, regardlessof the pick and place program that is being executed), nozzleperformance analytics system 302 can also be configured to track nozzlebehavior separately for respective different pick and place programs. Insuch embodiments, when generating performance vector data 802 for acurrent production run in which a selected pick and place program isbeing executed, data aggregation component 306 can exclude historicalperformance data generated during execution of other pick and placeprograms, and consider only historical performance data for the nozzleduring production runs in which the selected pick and place program wasexecuted. Similarly, vector analysis component 308 can maintain, foreach nozzle, both total accumulative model data for the nozzle acrossall programs, as well as separate sets of model data representingperformance of the nozzle during execution of respective different pickand place programs. Depending on the type of analysis requested by theuser, vector analysis component 308 can analyze performance vector data802 using either the cumulative (non-program-specific) model data forthe nozzle or the program-specific model data for the nozzle.

In some embodiments, rendering component 312 can set the size of eachbubble 904 to represent an accumulated number of rejects for thecorresponding nozzle across all programs in which the nozzle has beenused. In such embodiments, when graphical display 902 is set to renderprogram-specific positions of bubbles 904 within the x-y plane ofgraphical display 902, the y-axis position of each bubble 904 representsthe cumulative number of rejects for the currently executing pick andplace program only, while the size (radius) of bubble 904 represents thecumulative number of rejects for that nozzle across all programs. Inthis way, graphical display 902 can convey a view of bothprogram-specific nozzle performance and overall nozzle performancewithin the same graphical presentation. In some embodiments, the radiusof bubble 904 can be a function of multiple variables or measures overindividual time periods to highlight the reject intensity for a giventime period by program. In other embodiments, the radius of bubble 904can be a product of the number of rejects and the reject percentage forthe nozzle.

FIG. 10 is an example graphical interface 1022 that can be generated byone or more embodiments of rendering component 312. This exampleinterface 1022 can comprise several windows that summarize nozzleperformance based on analysis of the production data 812 as describedabove. In this example, graphical display 902 of FIG. 9 is included inthe bottom left corner of interface 1022. In the top left corner, amachine selection area 1002 renders interactive machine selectioncontrols that allow the user to select the set of nozzles whose data isto be displayed on the interface 1022. In this example, the machineselection area 1002 allows the user to select the plant and the pick andplace machine of interest. In response to receiving these selections,rendering component 312 can render statistics for nozzles of theselected machine on the interface 1022, including rendering the nozzlebehavior bubbles 904 corresponding to the nozzles of the selected pickand place machine.

Section 1004 renders information about the selected pick and placemachine, including the name of the machine, the current program beingexecuted by the machine, a date and time stamp of the most recentlyreceived set of production data 812, and other such information. Window1006 displays an overall reject rate for the machine as a graphicalgauge. Window 1020 renders a pie chart that breaks down the number ofassembly defects per head-spindle combination. Window 1008 rendersanother pie chart that conveys machine timer distribution. Window 1010renders detected defects as a function of timestamp. Window 1012 is adefect summary that lists the detected defects categorized according toa failure type and failure code.

Window 1014 renders a time-series graph plotting the nozzle reject ratefor the selected machine over time. Windows 1016 renders a table thatsummarizes, for each nozzle, the number of picks, number of placements,number of possible missing parts, the number of rejects, reject rates,and a computed reject factor for each nozzle.

FIGS. 11-12 illustrates methodologies in accordance with one or moreembodiments of the subject application. While, for purposes ofsimplicity of explanation, the methodologies shown herein is shown anddescribed as a series of acts, it is to be understood and appreciatedthat the subject innovation is not limited by the order of acts, as someacts may, in accordance therewith, occur in a different order and/orconcurrently with other acts from that shown and described herein. Forexample, those skilled in the art will understand and appreciate that amethodology could alternatively be represented as a series ofinterrelated states or events, such as in a state diagram. Moreover, notall illustrated acts may be required to implement a methodology inaccordance with the innovation. Furthermore, interaction diagram(s) mayrepresent methodologies, or methods, in accordance with the subjectdisclosure when disparate entities enact disparate portions of themethodologies. Further yet, two or more of the disclosed example methodscan be implemented in combination with each other, to accomplish one ormore features or advantages described herein.

FIG. 11 illustrates an example methodology 1100 for tracking performanceof pick and place nozzles and proactively notifying personnel inresponse to predicting a nozzle performance degradation in excess of anacceptable limit. Initially, at 1102, production data is collected fromone or more pick and place machines, the production data comprising atleast pick count information and reject count information for respectivenozzles of the pick and place machine. At 1104, for each nozzle of theone or more pick and place machines, an accumulated reject total and areject percentage is calculated based on the production data collectedat step 1102.

At 1106, a coordinate location within an x-y plane is calculated usingthe reject percentage calculated at step 1104 as the X position and theaccumulated reject total calculated at step 1104 as the Y position. At1108, a performance vector is generated for each nozzle based on atracking of the coordinate location within the x-y plane over time.

At 1110, predictive analysis is performed on the performance vectorgenerated at step 1108, and a future position of the coordinate locationwithin the x-y plane is estimated based on the predictive analysis. Insome embodiments, generation of the performance vector and performanceof the predictive analysis can be based on part on an accumulative modelof the nozzle's performance (in terms of total and percentage ofrejects) generated based on historical tracking of the coordinatelocation. Such analysis can consider the vector's slope, rate of change,and other such characteristics of the performance vector.

At 1112, a determination is made as to whether the future locationpredicted at step 1110 exceeds a boundary within the x-y plane. Theboundary can be, for example, an upper boundary on the accumulatednumber of rejects or an upper boundary on the reject percentage for thenozzle. If the future position is not predicted to exceed a boundary (NOat step 1112), the methodology returns to step 1102. Alternatively, ifthe future position is predicted to exceed the boundary (YES at step1112), the methodology proceeds to step 1114, where a notification isgenerated and delivered to a specified client device, the notificationidentifying the nozzle that is predicted to exceed the boundary. In someembodiments, the notification can also include information specifying anexpected time at which the nozzle is predicted to exceed the boundary, apossible root cause of the predicted performance degradation (e.g.,insufficient vacuum, an end of the nozzle's expected lifecycle, etc.), arecommended countermeasure for correcting the predicted issue, or othersuch information.

FIG. 12 illustrates an example methodology 1200 for tracking performanceof pick and place nozzles and proactively altering control of a pick andplace machine to slow or mitigate nozzle performance degradation. Steps1202-1212 are similar to corresponding steps 1102-1112 described abovein connection with FIG. 11. In this example, in response to determiningthat the estimated future position of the coordinate location for anozzle exceeds a preconfigured threshold indicative of an unacceptableperformance degradation (YES at step 1212), the methodology proceeds tostep 1214, where a control output is generated and directed to the pickand place machine in which the affected nozzle operates. The controloutput is directed to a control device of the pick and place machine,and is configured to alter operation of the pick and place machine tomitigate the predicted performance issue.

Embodiments, systems, and components described herein, as well asindustrial control systems and industrial automation environments inwhich various aspects set forth in the subject specification can becarried out, can include computer or network components such as servers,clients, programmable logic controllers (PLCs), automation controllers,communications modules, mobile computers, wireless components, controlcomponents and so forth which are capable of interacting across anetwork. Computers and servers include one or more processors—electronicintegrated circuits that perform logic operations employing electricsignals—configured to execute instructions stored in media such asrandom access memory (RAM), read only memory (ROM), a hard drives, aswell as removable memory devices, which can include memory sticks,memory cards, flash drives, external hard drives, and so on.

Similarly, the term PLC or automation controller as used herein caninclude functionality that can be shared across multiple components,systems, and/or networks. As an example, one or more PLCs or automationcontrollers can communicate and cooperate with various network devicesacross the network. This can include substantially any type of control,communications module, computer, Input/Output (I/O) device, sensor,actuator, and human machine interface (HMI) that communicate via thenetwork, which includes control, automation, and/or public networks. ThePLC or automation controller can also communicate to and control variousother devices such as standard or safety-rated I/O modules includinganalog, digital, programmed/intelligent I/O modules, other programmablecontrollers, communications modules, sensors, actuators, output devices,and the like.

The network can include public networks such as the internet, intranets,and automation networks such as control and information protocol (CIP)networks including DeviceNet, ControlNet, and Ethernet/IP. Othernetworks include Ethernet, DH/DH+, Remote I/O, Fieldbus, Modbus,Profibus, CAN, wireless networks, serial protocols, and so forth. Inaddition, the network devices can include various possibilities(hardware and/or software components). These include components such asswitches with virtual local area network (VLAN) capability, LANs, WANs,proxies, gateways, routers, firewalls, virtual private network (VPN)devices, servers, clients, computers, configuration tools, monitoringtools, and/or other devices.

In order to provide a context for the various aspects of the disclosedsubject matter, FIGS. 13 and 14 as well as the following discussion areintended to provide a brief, general description of a suitableenvironment in which the various aspects of the disclosed subject mattermay be implemented.

With reference to FIG. 13, an example environment 1310 for implementingvarious aspects of the aforementioned subject matter includes a computer1312. The computer 1312 includes a processing unit 1314, a system memory1316, and a system bus 1318. The system bus 1318 couples systemcomponents including, but not limited to, the system memory 1316 to theprocessing unit 1314. The processing unit 1314 can be any of variousavailable processors. Multi-core microprocessors and othermultiprocessor architectures also can be employed as the processing unit1314.

The system bus 1318 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, and/or a local bus using any variety of available busarchitectures including, but not limited to, 8-bit bus, IndustrialStandard Architecture (ISA), Micro-Channel Architecture (MSA), ExtendedISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Universal Serial Bus (USB),Advanced Graphics Port (AGP), Personal Computer Memory CardInternational Association bus (PCMCIA), and Small Computer SystemsInterface (SCSI).

The system memory 1316 includes volatile memory 1320 and nonvolatilememory 1322. The basic input/output system (BIOS), containing the basicroutines to transfer information between elements within the computer1312, such as during start-up, is stored in nonvolatile memory 1322. Byway of illustration, and not limitation, nonvolatile memory 1322 caninclude read only memory (ROM), programmable ROM (PROM), electricallyprogrammable ROM (EPROM), electrically erasable PROM (EEPROM), or flashmemory. Volatile memory 1320 includes random access memory (RAM), whichacts as external cache memory. By way of illustration and notlimitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), anddirect Rambus RAM (DRRAM).

Computer 1312 also includes removable/non-removable,volatile/non-volatile computer storage media. FIG. 13 illustrates, forexample a disk storage 1324. Disk storage 1324 includes, but is notlimited to, devices like a magnetic disk drive, floppy disk drive, tapedrive, Jaz drive, Zip drive, LS-100 drive, flash memory card, or memorystick. In addition, disk storage 1324 can include storage mediaseparately or in combination with other storage media including, but notlimited to, an optical disk drive such as a compact disk ROM device(CD-ROM), CD recordable drive (CD-R Drive), CD rewritable drive (CD-RWDrive) or a digital versatile disk ROM drive (DVD-ROM). To facilitateconnection of the disk storage 1324 to the system bus 1318, a removableor non-removable interface is typically used such as interface 1326.

It is to be appreciated that FIG. 13 describes software that acts as anintermediary between users and the basic computer resources described insuitable operating environment 1310. Such software includes an operatingsystem 1328. Operating system 1328, which can be stored on disk storage1324, acts to control and allocate resources of the computer 1312.System applications 1330 take advantage of the management of resourcesby operating system 1328 through program modules 1332 and program data1334 stored either in system memory 1316 or on disk storage 1324. It isto be appreciated that one or more embodiments of the subject disclosurecan be implemented with various operating systems or combinations ofoperating systems.

A user enters commands or information into the computer 1312 throughinput device(s) 1336. Input devices 1336 include, but are not limitedto, a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices connect to the processing unit 1314through the system bus 1318 via interface port(s) 1338. Interfaceport(s) 1338 include, for example, a serial port, a parallel port, agame port, and a universal serial bus (USB). Output device(s) 1340 usesome of the same type of ports as input device(s) 1336. Thus, forexample, a USB port may be used to provide input to computer 1312, andto output information from computer 1312 to an output device 1340.Output adapters 1342 are provided to illustrate that there are someoutput devices 1340 like monitors, speakers, and printers, among otheroutput devices 1340, which require special adapters. The output adapters1342 include, by way of illustration and not limitation, video and soundcards that provide a means of connection between the output device 1340and the system bus 1318. It should be noted that other devices and/orsystems of devices provide both input and output capabilities such asremote computer(s) 1344.

Computer 1312 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1344. The remote computer(s) 1344 can be a personal computer, a server,a router, a network PC, a workstation, a microprocessor based appliance,a peer device or other common network node and the like, and typicallyincludes many or all of the elements described relative to computer1312. For purposes of brevity, only a memory storage device 2246 isillustrated with remote computer(s) 1344. Remote computer(s) 1344 islogically connected to computer 1312 through a network interface 1348and then physically connected via communication connection 1350. Networkinterface 1348 encompasses communication networks such as local-areanetworks (LAN) and wide-area networks (WAN). LAN technologies includeFiber Distributed Data Interface (FDDI), Copper Distributed DataInterface (CDDI), Ethernet/IEEE 802.3, Token Ring/IEEE 802.5 and thelike. WAN technologies include, but are not limited to, point-to-pointlinks, circuit switching networks like Integrated Services DigitalNetworks (ISDN) and variations thereon, packet switching networks, andDigital Subscriber Lines (DSL).

Communication connection(s) 1350 refers to the hardware/softwareemployed to connect the network interface 1348 to the system bus 1318.While communication connection 1350 is shown for illustrative clarityinside computer 1312, it can also be external to computer 1312. Thehardware/software necessary for connection to the network interface 1348includes, for exemplary purposes only, internal and externaltechnologies such as, modems including regular telephone grade modems,cable modems and DSL modems, ISDN adapters, and Ethernet cards.

FIG. 14 is a schematic block diagram of a sample computing environment1400 with which the disclosed subject matter can interact. The samplecomputing environment 1400 includes one or more client(s) 1402. Theclient(s) 1402 can be hardware and/or software (e.g., threads,processes, computing devices). The sample computing environment 1400also includes one or more server(s) 1404. The server(s) 1404 can also behardware and/or software (e.g., threads, processes, computing devices).The servers 1404 can house threads to perform transformations byemploying one or more embodiments as described herein, for example. Onepossible communication between a client 1402 and servers 1404 can be inthe form of a data packet adapted to be transmitted between two or morecomputer processes. The sample computing environment 1400 includes acommunication framework 1406 that can be employed to facilitatecommunications between the client(s) 1402 and the server(s) 1404. Theclient(s) 1402 are operably connected to one or more client datastore(s) 1408 that can be employed to store information local to theclient(s) 1402. Similarly, the server(s) 1404 are operably connected toone or more server data store(s) 1410 that can be employed to storeinformation local to the servers 1404.

What has been described above includes examples of the subjectinnovation. It is, of course, not possible to describe every conceivablecombination of components or methodologies for purposes of describingthe disclosed subject matter, but one of ordinary skill in the art mayrecognize that many further combinations and permutations of the subjectinnovation are possible. Accordingly, the disclosed subject matter isintended to embrace all such alterations, modifications, and variationsthat fall within the spirit and scope of the appended claims.

In particular and in regard to the various functions performed by theabove described components, devices, circuits, systems and the like, theterms (including a reference to a “means”) used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., a functional equivalent), even though not structurallyequivalent to the disclosed structure, which performs the function inthe herein illustrated exemplary aspects of the disclosed subjectmatter. In this regard, it will also be recognized that the disclosedsubject matter includes a system as well as a computer-readable mediumhaving computer-executable instructions for performing the acts and/orevents of the various methods of the disclosed subject matter.

In addition, while a particular feature of the disclosed subject mattermay have been disclosed with respect to only one of severalimplementations, such feature may be combined with one or more otherfeatures of the other implementations as may be desired and advantageousfor any given or particular application. Furthermore, to the extent thatthe terms “includes,” and “including” and variants thereof are used ineither the detailed description or the claims, these terms are intendedto be inclusive in a manner similar to the term “comprising.”

In this application, the word “exemplary” is used to mean serving as anexample, instance, or illustration. Any aspect or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other aspects or designs. Rather, use of the wordexemplary is intended to present concepts in a concrete fashion.

Various aspects or features described herein may be implemented as amethod, apparatus, or article of manufacture using standard programmingand/or engineering techniques. The term “article of manufacture” as usedherein is intended to encompass a computer program accessible from anycomputer-readable device, carrier, or media. For example, computerreadable media can include but are not limited to magnetic storagedevices (e.g., hard disk, floppy disk, magnetic strips . . . ), opticaldisks [e.g., compact disk (CD), digital versatile disk (DVD) . . . ],smart cards, and flash memory devices (e.g., card, stick, key drive . .. ).

What is claimed is:
 1. A system for collecting and analyzing industrialperformance data, comprising: a processor that executes executablecomponents stored on a memory, the executable components comprising: adata aggregation component configured to generate performance vectordata for a nozzle of a pick and place machine based on production datacollected from the pick and place machine, wherein the performancevector data defines a location, over time, of a vector point of thenozzle within an x-y plane, and the data aggregation componentdetermines the location of the vector point as a plot of a total numberof rejects for the nozzle on a y-axis of the x-y plane and a percentageof rejects for the nozzle on an x-axis of the x-y plane; a vectoranalysis component configured to track a movement of the location of thevector point based on an analysis of the performance vector data; and anotification component configured to generate notification data directedto a client device in response to determining that the movement of thelocation of the vector point satisfies a defined criterion.
 2. Thesystem of claim 1, wherein the system is embodied on at least one of acloud platform or an edge device.
 3. The system of claim 1, furthercomprising a rendering component configured to render, on the clientdevice, a graph depicting the x-y plane and a plot of the vector pointfor the nozzle on the x-y plane.
 4. The system of claim 3, wherein thenotification data is first notification data, the vector analysiscomponent is further configured to predict a future trajectory of thelocation of the vector point based on an analysis of the performancevector data, and the notification component is further configured togenerate second notification data directed to the client device inresponse to determining that the future trajectory causes the locationof the vector point to exceed a preconfigured threshold.
 5. The systemof claim 3, wherein the data aggregation component is further configuredto separately track the total number of rejects for the nozzle and thepercentage of rejects for the nozzle for respective different programsexecuted by the pick and place machine, and the rendering component isconfigured to plot the vector point for the nozzle based on a subset ofthe production data corresponding to a program, of the differentprograms, currently executed by the pick and place machine.
 6. Thesystem of claim 5, wherein the rendering component is configured torender the vector point as a bubble on the graph depicting the x-yplane, wherein the bubble has a radius that is a function of multiplevariables or measures over individual time periods.
 7. The system ofclaim 1, wherein the vector analysis component is further configured to,in response to determining that the movement of the location of thevector point satisfies the defined criterion, generate a control outputdirected to the pick and place machine, and the control output isconfigured to alter an operation of the pick and place machine in amanner estimated to reduce a rate of rejects by the nozzle.
 8. Thesystem of claim 3, wherein the rendering component is further configuredto render, on the client device, a performance interface displaycomprising at least the graph depicting the x-y plane and a machineselection control that facilitates, via interaction with the machineselection control, selection of a selected pick and place machine of oneor more pick and place machines, and the selection of the selected pickand place machine causes the rendering component to render, on the graphdepicting the x-y plane, vector points for a set of nozzlescorresponding to the selected pick and place machine.
 9. The system ofclaim 5, wherein the vector analysis component is further configured torecord performance model data for the nozzle based on the analysis ofthe performance vector data, the performance model data modelsperformance of the nozzle as a function of the respective differentprograms, and the vector analysis component is configured to predict thefuture trajectory of the location based in part on the performance modeldata.
 10. The system of claim 1, wherein the defined criterion is atleast one of a defined direction of the movement of the location of thevector point or a defined rate of change of the movement of the locationof the vector point.
 11. A method, comprising: generating, by a systemcomprising a processor, performance vector data for a nozzle of a pickand place machine based on production data collected from the pick andplace machine, the performance vector data defining a location, as afunction of time, of a vector point of the nozzle within an x-y plane,wherein the generating comprises determining the location of the vectorpoint as a plot of a total number of rejects for the nozzle on a y-axisof the x-y plane and a percentage of rejects for the nozzle on an x-axisof the x-y plane; and in response to determining that a movement of thelocation of the vector point satisfies a defined criterion, generating,by the system, notification data directed to a client device.
 12. Themethod of claim 11, further comprising rendering, by the system on theclient device, a graphical representation of the x-y plane; andrendering, by the system on the client device, a plot of the vectorpoint for the nozzle on the graphical representation of the x-y plane.13. The method of claim 12, wherein the notification data is firstnotification data, and the method further comprises predicting, by thesystem, a future trajectory of the location of the vector point based onan analysis of the performance vector data, and in response topredicting that the future trajectory will cause the location of thevector point to exceed a defined threshold, generating, by the system,second notification data directed to the client device.
 14. The methodof claim 12, further comprising: tracking, by the system, the totalnumber of rejects for the nozzle and the percentage of rejects for thenozzle separately for respective different programs executed by the pickand place machine; and plotting, by the system, the vector point for thenozzle based on a subset of the production data corresponding to aprogram, of the different programs, currently executed by the pick andplace machine.
 15. The method of claim 14, wherein the rendering of theplot of the vector point comprises rendering the vector point on thegraphical representation of the x-y plane as a bubble having a radiusthat is a function of multiple variables or measures over individualtime periods.
 16. The method of claim 11, further comprising: inresponse to determining that the movement of the location of the vectorpoint satisfies the defined criterion, generating, by the system, acontrol output directed to the pick and place machine, wherein thecontrol output is configured to alter an operation of the pick and placemachine in a manner predicted to reduce a rate of rejects by the nozzle.17. The method of claim 11, further comprising recording, by the system,performance model data for the nozzle based on an analysis of theperformance vector data, wherein the performance model data modelsperformance of the nozzle as a function of the respective differentprograms.
 18. The method of claim 17, wherein the notification data isfirst notification data, and the method further comprises: predicting,by the system, a future trajectory of the location of the vector pointbased on an analysis of the performance vector data and the performancemodel data, and in response to predicting that the future trajectorywill cause the location of the vector point to exceed a definedthreshold, generating, by the system, second notification data directedto the client device.
 19. A non-transitory computer-readable mediumhaving stored thereon instructions that, in response to execution, causea system comprising a processor to perform operations, the operationscomprising: generating performance vector data for a nozzle of a pickand place machine based on analysis of production data generated by thepick and place machine, wherein the performance vector data defines alocation, as a function of time, of a vector point of the nozzle withinan x-y plane, and wherein the generating comprises determining, as thelocation of the vector point, a plot of a total number of rejects forthe nozzle along a y-axis of the x-y plane and a percentage of rejectsfor the nozzle along an x-axis of the x-y plane; tracking theperformance vector data to determine a movement of the location of thevector point over time; and in response to determining that the movementof the location of the vector point over time satisfies a definedcriterion, generating, by the system, notification data directed to aclient device.
 20. The non-transitory computer-readable medium of claim19, wherein the operations further comprise: rendering, on the clientdevice, a graph of the x-y plane; and rendering, on the client device, aplot of the vector point for the nozzle on the graph of the x-y plane.